https://danieledema.github.io/tags/reinforcement-learning/Recent content in Reinforcement-Learning on Daniele De MartiniHugoen-usThu, 15 Jun 2023 00:00:00 +0000https://danieledema.github.io/publications/20230615-roll_drop/Thu, 15 Jun 2023 00:00:00 +0000https://danieledema.github.io/publications/20230615-roll_drop/Abstract This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) – called Roll-Drop – that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state. DRL is a promising approach to control robots for highly dynamic and feedback-based manoeuvres, and accurate simulators are crucial to providing cheap and abundant data to learn the desired behaviour. Nevertheless, the simulated data are noiseless and generally show a distributional shift that challenges the deployment on real machines where sensor readings are affected by noise.https://danieledema.github.io/publications/20221116-metaverse_codesign/Wed, 16 Nov 2022 00:00:00 +0000https://danieledema.github.io/publications/20221116-metaverse_codesign/Abstract The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with the help of Mixed Reality (MR) technologies; still, to provide a satisfactory experience for users, the synchronization between the physical world and its digital models is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse.https://danieledema.github.io/publications/20211031-cpg_actor/Sun, 31 Oct 2021 00:00:00 +0000https://danieledema.github.io/publications/20211031-cpg_actor/Abstract Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Although conceptually promising, we argue that the full potential of CPGs has so far been limited by insufficient sensory-feedback information. This paper proposes a new methodology that allows tuning CPG controllers through gradient-based optimization in a Reinforcement Learning (RL) setting. To the best of our knowledge, this is the first time CPGs have been trained in conjunction with a MultilayerPerceptron (MLP) network in a Deep-RL context.